{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LANfactory` package is a light-weight convenience package for training `likelihood approximation networks` (LANs) in torch (or keras), \n",
    "starting from supplied training data.\n",
    "\n",
    "[LANs](https://elifesciences.org/articles/65074), although more general in potential scope of applications, were conceived in the context of sequential sampling modeling\n",
    "to account for cognitive processes giving rise to *choice* and *reaction time* data in *n-alternative forced choice experiments* commonly encountered in the cognitive sciences.\n",
    "\n",
    "In this quick tutorial we will use the [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package to generate our training data using such a sequential sampling model (SSM). The use of of the `LANfactory` package is in no way bound to utilize this `ssms` package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install\n",
    "\n",
    "To install the `ssms` package type,\n",
    "\n",
    "`pip install git+https://github.com/AlexanderFengler/ssm_simulators`\n",
    "\n",
    "To install the `LANfactory` package type,\n",
    "\n",
    "`pip install git+https://github.com/AlexanderFengler/LANfactory`\n",
    "\n",
    "Necessary dependency should be installed automatically in the process."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n"
     ]
    }
   ],
   "source": [
    "# Load necessary packages\n",
    "import ssms\n",
    "import lanfactory\n",
    "import os\n",
    "import numpy as np\n",
    "from copy import deepcopy\n",
    "import torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate Training Data\n",
    "First we need to generate some training data. As mentioned above we will do so using the `ssms` python package, however without delving into a detailed explanation\n",
    "of this package. Please refer to the [basic ssms tutorial] (https://github.com/AlexanderFengler/ssm_simulators) in case you want to learn more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE CONFIGS\n",
    "model = \"ddm_deadline\"\n",
    "# Initialize the generator config (for MLP LANs)\n",
    "generator_config = deepcopy(ssms.config.data_generator_config[\"lan\"])\n",
    "# Specify generative model (one from the list of included models mentioned above)\n",
    "generator_config[\"model\"] = model\n",
    "# Specify number of parameter sets to simulate\n",
    "generator_config[\"n_parameter_sets\"] = 10000\n",
    "# Specify how many samples a simulation run should entail\n",
    "generator_config[\"n_samples\"] = 1000\n",
    "# Specify folder in which to save generated data\n",
    "generator_config[\"output_folder\"] = \"data/lan_mlp/\" + model + \"/\"\n",
    "\n",
    "# Make model config dict\n",
    "model_config = ssms.config.model_config[model.split(\"_deadline\")[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'angle_deadline', 'params': ['v', 'a', 'z', 't', 'theta', 'deadline'], 'param_bounds': [[-3.0, 0.3, 0.1, 0.001, -0.1, 0.001], [3.0, 3.0, 0.9, 2.0, 1.3, 10]], 'boundary_name': 'angle', 'boundary': <function angle at 0x10724e9e0>, 'n_params': 6, 'default_params': [0.0, 1.0, 0.5, 0.001, 0.0, 10], 'nchoices': 2, 'simulator': <cyfunction ddm_flexbound at 0x1178ae400>}\n",
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     ]
    }
   ],
   "source": [
    "# MAKE DATA\n",
    "\n",
    "my_dataset_generator = ssms.dataset_generators.lan_mlp.data_generator(\n",
    "    generator_config=generator_config, model_config=model_config\n",
    ")\n",
    "\n",
    "for i in range(50):\n",
    "    print(i)\n",
    "    training_data = my_dataset_generator.generate_data_training_uniform(save=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Dataset completed'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Prepare for Training\n",
    "\n",
    "Next we set up dataloaders for training with pytorch. The `LANfactory` uses custom dataloaders, taking into account particularities of the expected training data.\n",
    "Specifically, we expect to receive a bunch of training data files (the present example generates only one), where each file hosts a large number of training examples. \n",
    "So we want to define a dataloader which spits out batches from data with a specific training data file, and keeps checking when to load in a new file. \n",
    "The way this is implemented here, is via the `DatasetTorch` class in `lanfactory.trainers`, which inherits from `torch.utils.data.Dataset` and prespecifies a `batch_size`. Finally this is supplied to a [`DataLoader`](https://pytorch.org/docs/stable/data.html), for which we keep the `batch_size` argument at 0.\n",
    "\n",
    "The `DatasetTorch` class is then called as an iterator via the DataLoader and takes care of batching as well as file loading internally. \n",
    "\n",
    "You may choose your own way of defining the `DataLoader` classes, downstream you are simply expected to supply one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MAKE DATALOADERS\n",
    "\n",
    "# List of datafiles (here only one)\n",
    "folder_ = \"data/lan_mlp/\" + model + \"/\"  # + \"/training_data_0_nbins_0_n_1000/\"\n",
    "file_list_ = [folder_ + file_ for file_ in os.listdir(folder_)]\n",
    "feature_key = \"opn_data\"\n",
    "label_key = \"opn_labels\"\n",
    "\n",
    "# Training dataset\n",
    "torch_training_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_ids=file_list_,\n",
    "    batch_size=2048,\n",
    "    features_key=feature_key,\n",
    "    label_key=label_key,\n",
    ")\n",
    "\n",
    "torch_training_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_training_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")\n",
    "\n",
    "# Validation dataset\n",
    "torch_validation_dataset = lanfactory.trainers.DatasetTorch(\n",
    "    file_ids=file_list_,\n",
    "    batch_size=2048,\n",
    "    features_key=feature_key,\n",
    "    label_key=label_key,\n",
    ")\n",
    "\n",
    "torch_validation_dataloader = torch.utils.data.DataLoader(\n",
    "    torch_validation_dataset,\n",
    "    shuffle=True,\n",
    "    batch_size=None,\n",
    "    num_workers=1,\n",
    "    pin_memory=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define two configuration dictionariers,\n",
    "\n",
    "1. The `network_config` dictionary defines the architecture and properties of the network\n",
    "2. The `train_config` dictionary defines properties concerning training hyperparameters\n",
    "\n",
    "Two examples (which we take as provided by the package, but which you can adjust according to your needs) are provided below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Network config: \n",
      "{'layer_sizes': [100, 100, 1], 'activations': ['tanh', 'tanh', 'linear'], 'train_output_type': 'logits'}\n",
      "Train config: \n",
      "{'cpu_batch_size': 128, 'gpu_batch_size': 256, 'n_epochs': 5, 'optimizer': 'adam', 'learning_rate': 0.002, 'lr_scheduler': 'reduce_on_plateau', 'lr_scheduler_params': {}, 'weight_decay': 0.0, 'loss': 'bcelogit', 'save_history': True}\n"
     ]
    }
   ],
   "source": [
    "# SPECIFY NETWORK CONFIGS AND TRAINING CONFIGS\n",
    "network_type = \"opn\"\n",
    "if network_type == \"cpn\":\n",
    "    network_config = lanfactory.config.network_configs.network_config_cpn\n",
    "    train_config = lanfactory.config.network_configs.train_config_cpn\n",
    "elif network_type == \"lan\":\n",
    "    network_config = lanfactory.config.network_configs.network_config_mlp\n",
    "    train_config = lanfactory.config.network_configs.train_config_mlp\n",
    "elif network_type == \"opn\":\n",
    "    network_config = lanfactory.config.network_configs.network_config_cpn\n",
    "    train_config = lanfactory.config.network_configs.train_config_cpn\n",
    "\n",
    "print(\"Network config: \")\n",
    "print(network_config)\n",
    "\n",
    "print(\"Train config: \")\n",
    "print(train_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now load a network, and save the configuration files for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "linear\n",
      "Found folder:  data\n",
      "Moving on...\n",
      "Found folder:  data/torch_models\n",
      "Moving on...\n",
      "Found folder:  data/torch_models/ddm_deadline\n",
      "Moving on...\n",
      "Saved network config\n",
      "Saved train config\n"
     ]
    }
   ],
   "source": [
    "# LOAD NETWORK\n",
    "network_config[\"layer_sizes\"] = [100, 100, 100, 100, 1]\n",
    "network_config[\"activations\"] = [\"tanh\", \"tanh\", \"tanh\", \"tanh\", \"linear\"]\n",
    "\n",
    "net = lanfactory.trainers.TorchMLP(\n",
    "    network_config=deepcopy(network_config),\n",
    "    input_shape=torch_training_dataset.input_dim,\n",
    "    save_folder=\"data/torch_models/\" + model + \"/\",\n",
    "    generative_model_id=\"angle\",\n",
    ")\n",
    "\n",
    "# SAVE CONFIGS\n",
    "lanfactory.utils.save_configs(\n",
    "    model_id=model + \"_torch_\",\n",
    "    save_folder=\"data/torch_models/\" + model + \"/\",\n",
    "    network_config=network_config,\n",
    "    train_config=train_config,\n",
    "    allow_abs_path_folder_generation=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_config\n",
    "\n",
    "# train_config[\"n_epochs\"] = 25"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finally train the network we supply our network, the dataloaders and training config to the `ModelTrainerTorchMLP` class, from `lanfactory.trainers`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Torch Device:  cpu\n",
      "Found folder:  data\n",
      "Moving on...\n",
      "Found folder:  data/torch_models\n",
      "Moving on...\n",
      "Found folder:  data/torch_models/ddm_deadline_opn\n",
      "Moving on...\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 0 / 20, batch: 0 / 200, batch_loss: 0.71523118019104\n",
      "epoch: 0 / 20, batch: 100 / 200, batch_loss: 0.15025952458381653\n",
      "Epoch took 0 / 20,  took 12.753804922103882 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 0 / 20, validation_loss: 0.1452\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 1 / 20, batch: 0 / 200, batch_loss: 0.1448168307542801\n",
      "epoch: 1 / 20, batch: 100 / 200, batch_loss: 0.14321260154247284\n",
      "Epoch took 1 / 20,  took 11.158228874206543 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 1 / 20, validation_loss: 0.143\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 2 / 20, batch: 0 / 200, batch_loss: 0.14219547808170319\n",
      "epoch: 2 / 20, batch: 100 / 200, batch_loss: 0.14337749779224396\n",
      "Epoch took 2 / 20,  took 11.419223070144653 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 2 / 20, validation_loss: 0.1406\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 3 / 20, batch: 0 / 200, batch_loss: 0.13764166831970215\n",
      "epoch: 3 / 20, batch: 100 / 200, batch_loss: 0.14669638872146606\n",
      "Epoch took 3 / 20,  took 11.496678113937378 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 3 / 20, validation_loss: 0.1391\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 4 / 20, batch: 0 / 200, batch_loss: 0.13720542192459106\n",
      "epoch: 4 / 20, batch: 100 / 200, batch_loss: 0.14131522178649902\n",
      "Epoch took 4 / 20,  took 11.933451652526855 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 4 / 20, validation_loss: 0.1399\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 5 / 20, batch: 0 / 200, batch_loss: 0.13003796339035034\n",
      "epoch: 5 / 20, batch: 100 / 200, batch_loss: 0.14236336946487427\n",
      "Epoch took 5 / 20,  took 11.153624773025513 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 5 / 20, validation_loss: 0.1384\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 6 / 20, batch: 0 / 200, batch_loss: 0.1319645792245865\n",
      "epoch: 6 / 20, batch: 100 / 200, batch_loss: 0.14233119785785675\n",
      "Epoch took 6 / 20,  took 11.260773181915283 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 6 / 20, validation_loss: 0.1389\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 7 / 20, batch: 0 / 200, batch_loss: 0.13935083150863647\n",
      "epoch: 7 / 20, batch: 100 / 200, batch_loss: 0.13755610585212708\n",
      "Epoch took 7 / 20,  took 11.15336012840271 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 7 / 20, validation_loss: 0.1387\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 8 / 20, batch: 0 / 200, batch_loss: 0.1388724446296692\n",
      "epoch: 8 / 20, batch: 100 / 200, batch_loss: 0.1474316418170929\n",
      "Epoch took 8 / 20,  took 11.9825119972229 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 8 / 20, validation_loss: 0.1395\n",
      "Epoch 00009: reducing learning rate of group 0 to 2.0000e-04.\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 9 / 20, batch: 0 / 200, batch_loss: 0.14344365894794464\n",
      "epoch: 9 / 20, batch: 100 / 200, batch_loss: 0.13674205541610718\n",
      "Epoch took 9 / 20,  took 11.224889993667603 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 9 / 20, validation_loss: 0.1382\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 10 / 20, batch: 0 / 200, batch_loss: 0.13591368496418\n",
      "epoch: 10 / 20, batch: 100 / 200, batch_loss: 0.13579222559928894\n",
      "Epoch took 10 / 20,  took 11.654073238372803 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 10 / 20, validation_loss: 0.138\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 11 / 20, batch: 0 / 200, batch_loss: 0.14045128226280212\n",
      "epoch: 11 / 20, batch: 100 / 200, batch_loss: 0.13924795389175415\n",
      "Epoch took 11 / 20,  took 11.1878981590271 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 11 / 20, validation_loss: 0.1379\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 12 / 20, batch: 0 / 200, batch_loss: 0.14119495451450348\n",
      "epoch: 12 / 20, batch: 100 / 200, batch_loss: 0.13047468662261963\n",
      "Epoch took 12 / 20,  took 11.368377208709717 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 12 / 20, validation_loss: 0.1392\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 13 / 20, batch: 0 / 200, batch_loss: 0.14141151309013367\n",
      "epoch: 13 / 20, batch: 100 / 200, batch_loss: 0.15125933289527893\n",
      "Epoch took 13 / 20,  took 11.102049112319946 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 13 / 20, validation_loss: 0.1379\n",
      "Epoch 00014: reducing learning rate of group 0 to 2.0000e-05.\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 14 / 20, batch: 0 / 200, batch_loss: 0.1490752100944519\n",
      "epoch: 14 / 20, batch: 100 / 200, batch_loss: 0.14018502831459045\n",
      "Epoch took 14 / 20,  took 11.48122787475586 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 14 / 20, validation_loss: 0.138\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 15 / 20, batch: 0 / 200, batch_loss: 0.14763256907463074\n",
      "epoch: 15 / 20, batch: 100 / 200, batch_loss: 0.14402344822883606\n",
      "Epoch took 15 / 20,  took 11.454025983810425 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 15 / 20, validation_loss: 0.1389\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 16 / 20, batch: 0 / 200, batch_loss: 0.13171446323394775\n",
      "epoch: 16 / 20, batch: 100 / 200, batch_loss: 0.13437706232070923\n",
      "Epoch took 16 / 20,  took 12.346750974655151 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 16 / 20, validation_loss: 0.1383\n",
      "Epoch 00017: reducing learning rate of group 0 to 2.0000e-06.\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 17 / 20, batch: 0 / 200, batch_loss: 0.1307690143585205\n",
      "epoch: 17 / 20, batch: 100 / 200, batch_loss: 0.14661775529384613\n",
      "Epoch took 17 / 20,  took 11.860939025878906 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 17 / 20, validation_loss: 0.1381\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 18 / 20, batch: 0 / 200, batch_loss: 0.14258289337158203\n",
      "epoch: 18 / 20, batch: 100 / 200, batch_loss: 0.12985864281654358\n",
      "Epoch took 18 / 20,  took 12.205140113830566 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 18 / 20, validation_loss: 0.1378\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch: 19 / 20, batch: 0 / 200, batch_loss: 0.14596745371818542\n",
      "epoch: 19 / 20, batch: 100 / 200, batch_loss: 0.14196249842643738\n",
      "Epoch took 19 / 20,  took 11.536289930343628 seconds\n",
      "passing 1\n",
      "wandb not available\n",
      "wandb not available\n",
      "epoch 19 / 20, validation_loss: 0.139\n",
      "Saving training history\n",
      "Saving training history to: data/torch_models/ddm_deadline_opn//ddm_deadline_cpn_runid_torch_training_history.csv\n",
      "Saving model state dict\n",
      "Saving model parameters to: data/torch_models/ddm_deadline_opn//ddm_deadline_cpn_runid_train_state_dict_torch.pt\n",
      "Saving training config to: data/torch_models/ddm_deadline_opn//ddm_deadline_cpn_runid_train_config.pickle\n",
      "Saving training data details to: data/torch_models/ddm_deadline_opn//ddm_deadline_cpn_runid_data_details.pickle\n",
      "Training finished successfully...\n"
     ]
    }
   ],
   "source": [
    "# TRAIN MODEL\n",
    "n_epochs = 20\n",
    "train_config[\"n_epochs\"] = n_epochs\n",
    "\n",
    "model_trainer = lanfactory.trainers.ModelTrainerTorchMLP(\n",
    "    model=net,\n",
    "    train_config=train_config,\n",
    "    train_dl=torch_training_dataloader,\n",
    "    valid_dl=torch_validation_dataloader,\n",
    "    allow_abs_path_folder_generation=False,\n",
    "    pin_memory=True,\n",
    "    seed=None,\n",
    ")\n",
    "\n",
    "# model_trainer.train_model(save_history=True, save_model=True, verbose=0)\n",
    "model_trainer.train_and_evaluate(\n",
    "    wandb_on=0,\n",
    "    output_folder=\"data/torch_models/\" + model + \"_opn/\",\n",
    "    output_file_id=model,\n",
    ")\n",
    "# LOAD MODEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load Model for Inference and Call"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `LANfactory` provides some convenience functions to use networks for inference after training. \n",
    "We can load a model using the `LoadTorchMLPInfer` class, which then allows us to run fast inference via either\n",
    "a direct call, which expects a `torch.tensor` as input, or the `predict_on_batch` method, which expects a `numpy.array` \n",
    "of `dtype`, `np.float32`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "tanh\n",
      "linear\n"
     ]
    }
   ],
   "source": [
    "network_path_list = os.listdir(\"data/torch_models/\" + model + \"_opn\")\n",
    "network_file_path = [\n",
    "    \"data/torch_models/\" + model + \"_opn\" + \"/\" + file_\n",
    "    for file_ in network_path_list\n",
    "    if \"state_dict\" in file_\n",
    "][0]\n",
    "\n",
    "network = lanfactory.trainers.LoadTorchMLPInfer(\n",
    "    model_file_path=network_file_path,\n",
    "    network_config=network_config,\n",
    "    input_dim=torch_training_dataset.input_dim,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "direct call out:  tensor([-0.0048])\n",
      "predict_on_batch out:  [-0.00476831]\n"
     ]
    }
   ],
   "source": [
    "# Two ways to call the network\n",
    "\n",
    "# Direct call --> need tensor input\n",
    "direct_out = network(\n",
    "    torch.from_numpy(np.array([1, 1.5, 0.5, 1.0, 1], dtype=np.float32))\n",
    ")\n",
    "print(\"direct call out: \", direct_out)\n",
    "\n",
    "# predict_on_batch method\n",
    "predict_on_batch_out = network.predict_on_batch(\n",
    "    np.array([1, 1.5, 0.5, 1.0, 1], dtype=np.float32)\n",
    ")\n",
    "print(\"predict_on_batch out: \", predict_on_batch_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A peek into the first passage distribution computed by the network\n",
    "\n",
    "We can compare the learned likelihood function in our `network` with simulation data from the underlying generative model.\n",
    "For this purpose we recruit the [`ssms`](https://github.com/AlexanderFengler/ssm_simulators) package again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/ssms/basic_simulators/simulator.py:399: UserWarning: Deadline model request, and theta not supplied as dict.Make sure to supply the deadline parameters in last position!\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.zeros((2000, 5), dtype=np.float32),\n",
    "    columns=[\"v\", \"a\", \"z\", \"t\", \"deadline\"],\n",
    ")\n",
    "data[\"v\"] = np.linspace(-2.0, 2.0, 2000)\n",
    "data[\"a\"] = 2.0\n",
    "data[\"z\"] = 0.5\n",
    "data[\"t\"] = 0.5\n",
    "# data[\"theta\"] = 0.0\n",
    "data[\"deadline\"] = 2.0\n",
    "\n",
    "# Network predictions\n",
    "network_predictions = []\n",
    "\n",
    "# Simulations\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "\n",
    "omission_p_vec = np.zeros((20, 40))  # np.zeros(20, 20)\n",
    "for i, deadline in enumerate(np.linspace(0.5, 2.5, 20)):\n",
    "    data[\"v\"] = np.linspace(-2.0, 2.0, 2000)\n",
    "    data[\"deadline\"] = deadline\n",
    "    network_predictions.append(network.predict_on_batch(data.values.astype(np.float32)))\n",
    "    for j, v in enumerate(np.linspace(-2.0, 2.0, 40)):\n",
    "        data[\"v\"] = v\n",
    "        sim_out = simulator(model=model, theta=data.values[0, :], n_samples=5000)\n",
    "        omission_p_vec[i, j] = sim_out[\"omission_p\"][0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sim_out['omission_p']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'omission_p')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot network predictions\n",
    "# Plot simulations\n",
    "for i in range(20):\n",
    "    plt.plot(\n",
    "        np.linspace(-2, 2, 2000),\n",
    "        np.exp(network_predictions[i]),\n",
    "        color=\"black\",\n",
    "        label=None if i > 0 else \"network\",\n",
    "        alpha=i / 20,\n",
    "    )\n",
    "\n",
    "    plt.plot(\n",
    "        np.linspace(-2, 2, 40),\n",
    "        np.array(omission_p_vec[i, :]),\n",
    "        color=\"red\",\n",
    "        label=None if i > 0 else \"simulations\",\n",
    "        alpha=i / 20,\n",
    "    )\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"SSM likelihood: alpha increases with angle of collapse\")\n",
    "plt.xlabel(\"v\")\n",
    "plt.ylabel(\"omission_p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.46153846,  2.        ,  0.5       ,  0.5       ,  1.55263158])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.values[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ddm_deadline'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['v', 'a', 'z', 't', 'deadline']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2. ,  2. ,  0.5,  0.5,  0.5], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([data[k] for k in my_params]).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['v', 'a', 'z', 't', 'deadline', 'deadline']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'network' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb Cell 33\u001b[0m line \u001b[0;36m4\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m data[\u001b[39m\"\u001b[39m\u001b[39mdeadline\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m deadline\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=39'>40</a>\u001b[0m data[\u001b[39m\"\u001b[39m\u001b[39mv\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m v\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m network_predictions[i, j] \u001b[39m=\u001b[39m network\u001b[39m.\u001b[39mpredict_on_batch(np\u001b[39m.\u001b[39marray([data[k] \u001b[39mfor\u001b[39;00m k \u001b[39min\u001b[39;00m my_params])\u001b[39m.\u001b[39mastype(np\u001b[39m.\u001b[39mfloat32))\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=42'>43</a>\u001b[0m sim_out \u001b[39m=\u001b[39m simulator(model\u001b[39m=\u001b[39mmodel, \n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m                     theta\u001b[39m=\u001b[39mdata, \n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m                     n_samples\u001b[39m=\u001b[39m\u001b[39m2000\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/afengler/Library/CloudStorage/OneDrive-Personal/project_lanfactory/LANfactory/notebooks/basic_tutorial_opn.ipynb#Y113sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m omission_p_vec[i, j] \u001b[39m=\u001b[39m sim_out[\u001b[39m\"\u001b[39m\u001b[39momission_p\u001b[39m\u001b[39m\"\u001b[39m][\u001b[39m0\u001b[39m, \u001b[39m0\u001b[39m]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'network' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.zeros((2000, 5), dtype=np.float32),\n",
    "    columns=[\"v\", \"a\", \"z\", \"t\", \"deadline\"],\n",
    ")\n",
    "\n",
    "data = {\"v\": 0, \"a\": 2.0, \"z\": 0.5, \"t\": 0.5, \"deadline\": 2.0}\n",
    "\n",
    "my_params = deepcopy(ssms.config.model_config[model.split(\"_deadline\")[0]][\"params\"])\n",
    "my_params.append(\"deadline\")\n",
    "\n",
    "# data[\"v\"] = np.linspace(-2., 2., 2000)\n",
    "# data[\"a\"] = 2.0\n",
    "# data[\"z\"] = 0.5\n",
    "# data[\"t\"] = 0.5\n",
    "# data[\"theta\"] = 0.0\n",
    "# data[\"deadline\"] = 2.\n",
    "\n",
    "# Network predictions\n",
    "# network_predictions = []\n",
    "\n",
    "# Simulations\n",
    "omission_p_vec = np.zeros((5, 40))  # np.zeros(20, 20)\n",
    "network_predictions = np.zeros((5, 40))\n",
    "\n",
    "for i, deadline in enumerate(np.linspace(0.5, 1.0, 5)):\n",
    "    # data[\"v\"] = np.linspace(-2., 2., 2000)\n",
    "    # data[\"deadline\"] = deadline\n",
    "    # network_predictions.append(network.predict_on_batch(data.values.astype(np.float32)))\n",
    "    for j, v in enumerate(np.linspace(-2.0, 2.0, 40)):\n",
    "        data[\"deadline\"] = deadline\n",
    "        data[\"v\"] = v\n",
    "\n",
    "        network_predictions[i, j] = network.predict_on_batch(\n",
    "            np.array([data[k] for k in my_params]).astype(np.float32)\n",
    "        )\n",
    "        sim_out = simulator(model=model, theta=data, n_samples=2000)\n",
    "\n",
    "        omission_p_vec[i, j] = sim_out[\"omission_p\"][0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'omission_p')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot network predictions\n",
    "# Plot simulations\n",
    "for i in range(5):\n",
    "    plt.plot(\n",
    "        np.linspace(-2, 2, 40),\n",
    "        np.exp(network_predictions[i, :]),\n",
    "        color=\"black\",\n",
    "        label=None if i > 0 else \"network\",\n",
    "        alpha=min((i + 1) / 5, 1),\n",
    "    )\n",
    "    plt.plot(\n",
    "        np.linspace(-2, 2, 40),\n",
    "        np.array(omission_p_vec[i, :]),\n",
    "        color=\"red\",\n",
    "        label=None if i > 0 else \"simulations\",\n",
    "        alpha=min((i + 1) / 5, 1),\n",
    "    )\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"SSM likelihood: alpha increases with angle of collapse\")\n",
    "plt.xlabel(\"v\")\n",
    "plt.ylabel(\"omission_p\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.5]\n",
      "[0.5025126]\n",
      "[0.50502515]\n",
      "[0.50753766]\n",
      "[0.51005024]\n",
      "[0.5125628]\n",
      "[0.5150754]\n",
      "[0.51758796]\n",
      "[0.5201005]\n",
      "[0.52261305]\n",
      "[0.5251256]\n",
      "[0.5276382]\n",
      "[0.5301508]\n",
      "[0.53266335]\n",
      "[0.53517586]\n",
      "[0.53768843]\n",
      "[0.540201]\n",
      "[0.5427136]\n",
      "[0.54522616]\n",
      "[0.5477387]\n",
      "[0.55025125]\n",
      "[0.5527638]\n",
      "[0.5552764]\n",
      "[0.55778897]\n",
      "[0.5603015]\n",
      "[0.56281406]\n",
      "[0.56532663]\n",
      "[0.5678392]\n",
      "[0.5703518]\n",
      "[0.5728643]\n",
      "[0.57537687]\n",
      "[0.57788944]\n",
      "[0.580402]\n",
      "[0.5829146]\n",
      "[0.58542717]\n",
      "[0.5879397]\n",
      "[0.59045225]\n",
      "[0.5929648]\n",
      "[0.5954774]\n",
      "[0.59799]\n",
      "[0.6005025]\n",
      "[0.60301507]\n",
      "[0.60552764]\n",
      "[0.6080402]\n",
      "[0.6105528]\n",
      "[0.6130653]\n",
      "[0.6155779]\n",
      "[0.61809045]\n",
      "[0.620603]\n",
      "[0.6231156]\n",
      "[0.6256281]\n",
      "[0.6281407]\n",
      "[0.63065326]\n",
      "[0.63316584]\n",
      "[0.6356784]\n",
      "[0.6381909]\n",
      "[0.6407035]\n",
      "[0.6432161]\n",
      "[0.64572865]\n",
      "[0.6482412]\n",
      "[0.6507538]\n",
      "[0.6532663]\n",
      "[0.6557789]\n",
      "[0.65829146]\n",
      "[0.66080403]\n",
      "[0.6633166]\n",
      "[0.6658291]\n",
      "[0.6683417]\n",
      "[0.6708543]\n",
      "[0.67336684]\n",
      "[0.6758794]\n",
      "[0.67839193]\n",
      "[0.6809045]\n",
      "[0.6834171]\n",
      "[0.68592966]\n",
      "[0.68844223]\n",
      "[0.69095474]\n",
      "[0.6934673]\n",
      "[0.6959799]\n",
      "[0.69849247]\n",
      "[0.70100504]\n",
      "[0.7035176]\n",
      "[0.70603013]\n",
      "[0.7085427]\n",
      "[0.7110553]\n",
      "[0.71356785]\n",
      "[0.7160804]\n",
      "[0.71859294]\n",
      "[0.7211055]\n",
      "[0.7236181]\n",
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      "[0.73115575]\n",
      "[0.7336683]\n",
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      "[0.7386935]\n",
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      "[0.74371856]\n",
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      "[0.8065327]\n",
      "[0.80904526]\n",
      "[0.81155777]\n",
      "[0.81407034]\n",
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      "[0.8492462]\n",
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      "[0.85427135]\n",
      "[0.8567839]\n",
      "[0.8592965]\n",
      "[0.8618091]\n",
      "[0.8643216]\n",
      "[0.86683416]\n",
      "[0.86934674]\n",
      "[0.8718593]\n",
      "[0.8743719]\n",
      "[0.8768844]\n",
      "[0.879397]\n",
      "[0.88190955]\n",
      "[0.8844221]\n",
      "[0.8869347]\n",
      "[0.8894472]\n",
      "[0.8919598]\n",
      "[0.89447236]\n",
      "[0.89698493]\n",
      "[0.8994975]\n",
      "[0.90201]\n",
      "[0.9045226]\n",
      "[0.9070352]\n",
      "[0.90954775]\n",
      "[0.9120603]\n",
      "[0.91457283]\n",
      "[0.9170854]\n",
      "[0.919598]\n",
      "[0.92211056]\n",
      "[0.92462313]\n",
      "[0.9271357]\n",
      "[0.9296482]\n",
      "[0.9321608]\n",
      "[0.93467337]\n",
      "[0.93718594]\n",
      "[0.9396985]\n",
      "[0.94221103]\n",
      "[0.9447236]\n",
      "[0.9472362]\n",
      "[0.94974875]\n",
      "[0.9522613]\n",
      "[0.95477384]\n",
      "[0.9572864]\n",
      "[0.959799]\n",
      "[0.96231157]\n",
      "[0.96482414]\n",
      "[0.96733665]\n",
      "[0.9698492]\n",
      "[0.9723618]\n",
      "[0.9748744]\n",
      "[0.97738695]\n",
      "[0.9798995]\n",
      "[0.98241204]\n",
      "[0.9849246]\n",
      "[0.9874372]\n",
      "[0.98994976]\n",
      "[0.99246234]\n",
      "[0.99497485]\n",
      "[0.9974874]\n",
      "[1.]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from ssms.basic_simulators.simulator import simulator\n",
    "\n",
    "data = pd.DataFrame(\n",
    "    np.zeros((2000, 5), dtype=np.float32),\n",
    "    columns=[\"v\", \"a\", \"z\", \"t\", \"deadline\"],\n",
    ")\n",
    "\n",
    "data = {\"v\": 0, \"a\": 1.0, \"z\": 0.5, \"t\": 0.5, \"deadline\": 2.0}\n",
    "\n",
    "my_params = deepcopy(ssms.config.model_config[model.split(\"_deadline\")[0]][\"params\"])\n",
    "my_params.append(\"deadline\")\n",
    "\n",
    "# data[\"v\"] = np.linspace(-2., 2., 2000)\n",
    "# data[\"a\"] = 2.0\n",
    "# data[\"z\"] = 0.5\n",
    "# data[\"t\"] = 0.5\n",
    "# data[\"theta\"] = 0.0\n",
    "# data[\"deadline\"] = 2.\n",
    "\n",
    "# Network predictions\n",
    "# network_predictions = []\n",
    "\n",
    "# Simulations\n",
    "omission_p_vec = np.zeros((200))  # np.zeros(20, 20)\n",
    "\n",
    "for i, deadline in enumerate(np.linspace(0.5, 1.0, 200)):\n",
    "    # data[\"v\"] = np.linspace(-2., 2., 2000)\n",
    "    # data[\"deadline\"] = deadline\n",
    "    # network_predictions.append(network.predict_on_batch(data.values.astype(np.float32)))\n",
    "\n",
    "    data[\"deadline\"] = deadline\n",
    "    # data[\"v\"] = v\n",
    "\n",
    "    # network_predictions[i, j] = network.predict_on_batch(np.array([data[k] for k in my_params]).astype(np.float32))\n",
    "    sim_out = simulator(model=model, theta=data, n_samples=2000)\n",
    "\n",
    "    omission_p_vec[i] = sim_out[\"omission_p\"][0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.]\n"
     ]
    }
   ],
   "source": [
    "sim_out = simulator(model=model, theta=data, n_samples=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float32)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim_out[\"rts\"][sim_out[\"rts\"] >= 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'omission_p')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    np.linspace(0.5, 1.0, 200),\n",
    "    np.array(omission_p_vec),\n",
    "    color=\"red\",\n",
    "    label=None if i > 0 else \"simulations\",\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.title(\"SSM likelihood: alpha increases with angle of collapse\")\n",
    "plt.xlabel(\"v\")\n",
    "plt.ylabel(\"omission_p\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We hope this package may be helpful in case you attempt to train [LANs](https://elifesciences.org/articles/65074) for your own research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### END"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 ('cssm')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "c2404e761a8d4e2a34f63613cf4c9a9997cd3109cabb959a7904b2035989131a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
